Order Article Reprints

Santoro M, Zybin V, Coada CA, Mantovani G, Paolani G, Di Stanislao M, Modolon C, Di Costanzo S, Lebovici A, Ravegnini G, et al. Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study. Cancers. 2024; 16(8):1570. https://doi.org/10.3390/cancers16081570

Santoro, Miriam, Vladislav Zybin, Camelia Alexandra Coada, Giulia Mantovani, Giulia Paolani, Marco Di Stanislao, Cecilia Modolon, Stella Di Costanzo, Andrei Lebovici, Gloria Ravegnini, and et al. 2024. "Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study" Cancers 16, no. 8: 1570. https://doi.org/10.3390/cancers16081570

Santoro, M., Zybin, V., Coada, C. A., Mantovani, G., Paolani, G., Di Stanislao, M., Modolon, C., Di Costanzo, S., Lebovici, A., Ravegnini, G., De Leo, A., Tesei, M., Pasquini, P., Lovato, L., Morganti, A. G., Pantaleo, M. A., De Iaco, P., Strigari, L., & Perrone, A. M. (2024). Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study. Cancers, 16(8), 1570. https://doi.org/10.3390/cancers16081570